Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for providing and aggregating EEG bioelectrical signal data, using a first EEG biosignal neuroheadset and a second EEG biosignal neuroheadset, the method comprising: establishing a first electrical interface between the first EEG biosignal neuroheadset and a body region of a first user proximal a head region of the first user; establishing a second electrical interface between the second EEG biosignal neuroheadset and a body region of a second user proximal a head region of the second user; providing a stimulus to the first user and to the second user, wherein the stimulus is configured to prompt an action in both the first user and the second user; at a first EEG sensor system of the first EEG biosignal neuroheadset, automatically collecting a first EEG bioelectrical signal dataset from the first user as the first user performs the action; at a first motion sensor system of the first EEG biosignal neuroheadset, collecting a first motion dataset from the first user as the first user performs the action; at a second EEG sensor system of the second EEG biosignal neuroheadset, automatically collecting a second EEG bioelectrical signal dataset from the second user as the second user performs the action; at a second motion sensor system of the second EEG biosignal neuroheadset, collecting a second motion dataset from the second user as the second user performs the action; generating a first anonymized EEG bioelectrical signal dataset from the first EEG bioelectrical signal dataset and a second anonymized EEG bioelectrical signal dataset from the second EEG bioelectrical signal dataset; coupling the first and the second anonymized EEG bioelectrical signal datasets with an action tag characterizing the action; and generating an analysis based upon the first and the second motion datasets and the first and the second anonymized EEG bioelectrical signal datasets, wherein the analysis facilitates elicitation of target brain activity.
2. The method of claim 1 , wherein providing a stimulus comprises providing the stimulus at a first mobile device of the first user and providing the stimulus at a second mobile device of the second user.
This invention relates to a system for facilitating interactions between users, particularly in a social or collaborative context. The problem addressed is the need to synchronize or coordinate stimuli (e.g., notifications, alerts, or prompts) across multiple devices to enhance user engagement or collaboration. The method involves providing a stimulus to at least two users, where the stimulus is delivered simultaneously or in a coordinated manner to both a first mobile device of the first user and a second mobile device of the second user. This ensures that both users receive the stimulus at the same time, which may be useful for activities requiring real-time coordination, such as collaborative tasks, social interactions, or synchronized notifications. The stimulus can be any form of digital signal, such as a notification, alert, or prompt, and may be tailored to the context of the interaction. The method may also include additional steps such as detecting user responses or adjusting the stimulus based on user behavior. The invention aims to improve user engagement and coordination by ensuring that stimuli are delivered consistently across multiple devices.
3. The method of claim 1 , wherein providing a stimulus comprises automatically providing the stimulus in synchronization with additional biosignal data collected from at least one of the first user and the second user.
This invention relates to systems for enhancing communication between users, particularly in scenarios where traditional communication methods are impaired, such as in virtual reality (VR) or augmented reality (AR) environments. The problem addressed is the lack of natural, real-time feedback mechanisms that convey emotional or physiological states between users, which can hinder effective interaction. The method involves collecting biosignal data from at least one user, such as heart rate, skin conductance, or brain activity, to infer their emotional or physiological state. A stimulus, such as a visual, auditory, or haptic cue, is then automatically provided to another user in synchronization with the collected biosignal data. This stimulus serves as an indirect communication channel, allowing the second user to perceive the first user's state without direct verbal or explicit input. The synchronization ensures that the stimulus accurately reflects the real-time physiological responses of the first user, enhancing the immediacy and authenticity of the interaction. The method may also involve processing the biosignal data to filter or interpret specific patterns, ensuring that only relevant or meaningful signals trigger the stimulus. This can include distinguishing between different emotional states or physiological conditions. The stimulus can be customized based on the context of the interaction, such as adjusting the intensity or type of cue to match the environment or the users' preferences. The system may also adapt over time, learning from user responses to improve the accuracy and relevance of the stimuli provided. This approach enables more intuitive and empathetic communication in digital or remote interactions.
4. The method of claim 1 , wherein automatically collecting a first bioelectrical signal dataset from the first user as the first user performs the action and automatically collecting a second bioelectrical signal dataset from the second user as the second user performs the action comprise automatically detecting when the first user and the second user initiate performance of the action, automatically detecting when the first user and the second user terminate performance of the action, and collecting the first and the second bioelectrical signal dataset between initiation and termination of the action for each of the first user and the second user.
This invention relates to bioelectrical signal analysis for comparing physiological responses between users performing the same action. The technology addresses the challenge of accurately capturing and synchronizing bioelectrical data from multiple individuals during specific activities to enable meaningful comparative analysis. The method involves automatically detecting when each user begins and ends a predefined action, then continuously collecting bioelectrical signals from both users throughout the action's duration. This ensures that the collected datasets are time-aligned and correspond precisely to the period of active performance. The system may use sensors to monitor electrical activity such as muscle activation, neural signals, or other bioelectrical phenomena. By synchronizing data collection with action initiation and termination, the method provides consistent datasets for analyzing differences in physiological responses between individuals. This approach is particularly useful in applications like rehabilitation monitoring, performance assessment, or medical diagnostics where comparative bioelectrical data is valuable. The automated detection of action boundaries eliminates manual intervention, improving data reliability and reducing variability in multi-user studies.
5. The method of claim 4 , wherein automatically detecting comprises automatically detecting at a sensor system configured to receive at least one of biosignal data and environment data.
This invention relates to a method for automatically detecting conditions using biosignal and environmental data. The method involves using a sensor system to collect at least one of biosignal data (e.g., heart rate, EEG, EMG) or environment data (e.g., temperature, humidity, air quality). The sensor system processes this data to identify specific conditions, such as physiological states (e.g., stress, fatigue) or environmental factors (e.g., hazardous conditions). The detection process may involve analyzing patterns, thresholds, or machine learning models to determine the presence of these conditions. The method ensures real-time or near-real-time monitoring, enabling timely interventions or adjustments. The sensor system may be wearable, embedded in devices, or part of a larger monitoring network. The invention addresses the need for automated, accurate, and continuous detection of conditions that impact health, safety, or performance in various applications, such as medical monitoring, industrial safety, or smart environments. The method improves upon traditional manual detection by reducing human error and increasing responsiveness.
6. The method of claim 1 , further comprising: providing a second stimulus configured to prompt a second action in both the first user and the second user; at the first biosignal detector, automatically collecting a first repeat bioelectrical signal dataset from the first user as the first user performs the second action; and at the second biosignal detector, automatically collecting a second repeat bioelectrical signal dataset from the second user as the second user performs the second action.
This invention relates to a system for analyzing bioelectrical signals from multiple users to assess their responses to stimuli. The problem addressed is the need to accurately measure and compare physiological reactions between individuals when subjected to controlled stimuli, such as actions or events. The system includes at least two biosignal detectors, each configured to collect bioelectrical signals from a respective user. A stimulus is provided to prompt a specific action in both users, and their bioelectrical signals are automatically recorded as they perform the action. The system further includes a processing unit that analyzes the collected bioelectrical signals to determine correlations or differences in the users' responses. Additionally, a second stimulus is provided to prompt a second action, and the bioelectrical signals of both users are again collected as they perform this second action. This allows for repeated measurements to improve accuracy and reliability. The system may be used in applications such as psychological studies, medical diagnostics, or human-computer interaction research, where understanding physiological responses to stimuli is critical. The invention ensures consistent data collection and analysis across multiple users and stimuli, enabling more robust conclusions about their physiological reactions.
7. The method of claim 6 , further comprising generating a comparative analysis based upon at least two of the first bioelectrical signal dataset, the second bioelectrical signal dataset, the first repeat bioelectrical signal dataset, and the second repeat bioelectrical signal dataset.
This invention relates to bioelectrical signal analysis, specifically for comparing datasets to assess consistency or changes over time. The method involves collecting at least two initial bioelectrical signal datasets from a subject, followed by collecting repeat bioelectrical signal datasets at a later time. The collected datasets are processed to extract relevant features, such as signal amplitude, frequency, or timing characteristics. A comparative analysis is then generated by comparing at least two of the datasets—either initial or repeat—to identify differences or similarities. This analysis may include statistical comparisons, trend analysis, or pattern recognition to evaluate physiological changes, treatment efficacy, or device performance. The method enables tracking bioelectrical signal variations over time, which is useful in medical diagnostics, research, or monitoring applications. The comparative analysis helps determine whether observed changes are significant or within expected variability, improving the reliability of bioelectrical signal assessments.
8. The method of claim 6 , wherein the stimulus is identical to the second stimulus.
9. The method of claim 1 , wherein generating an analysis comprises generating a comparative analysis characterizing a demographic group comprising the first user and the second user.
This invention relates to user analytics and personalized content delivery, specifically addressing the problem of understanding and leveraging group user characteristics for improved service. The core of the technology involves a method for analyzing user data. A key aspect of this analysis is the generation of a comparative analysis. This comparative analysis is designed to characterize a demographic group. This demographic group is specifically defined as comprising at least two users, referred to as a first user and a second user. The purpose of this comparative analysis is to identify shared or contrasting demographic attributes between these users, thereby creating a profile for the group. This group profile can then be used for various applications, such as tailoring content, recommendations, or services to the collective needs or preferences of the identified demographic. The method enables a deeper understanding of user interactions by moving beyond individual analysis to group-level insights.
10. The method of claim 1 , wherein the first EEG sensor system comprises a first plurality of EEG sensors corresponding to a first plurality of channels, wherein the first EEG bioelectrical signal dataset comprises first multi-channel EEG bioelectrical signal data, wherein the second EEG sensor system comprises a second plurality of EEG sensors corresponding to a second plurality of channels, wherein the second EEG bioelectrical signal dataset comprises second multi-channel EEG bioelectrical signal data, and wherein generating the analysis comprises generating the analysis based on the first and the second motion datasets and the first and the second multi-channel EEG bioelectrical signal datasets.
This invention relates to a system for analyzing brain activity using multiple electroencephalography (EEG) sensor systems. The technology addresses the challenge of obtaining comprehensive and accurate EEG data by integrating signals from multiple sensor arrays to improve analysis of brain function. The system includes a first EEG sensor system with multiple sensors arranged in a first set of channels, capturing multi-channel EEG bioelectrical signals from a subject. A second EEG sensor system, with a separate set of sensors and channels, captures additional multi-channel EEG data. Both systems generate motion datasets to account for movement artifacts. The analysis process combines the motion data and multi-channel EEG signals from both sensor systems to produce a more robust and detailed assessment of brain activity. This approach enhances the reliability and accuracy of EEG-based diagnostics and research by leveraging redundant and complementary data from multiple sensor arrays. The method ensures that motion artifacts are minimized and that the combined datasets provide a more comprehensive representation of neural activity.
11. The method of claim 10 , wherein the first motion sensor system comprises a first accelerometer and a first gyroscope, wherein the first motion dataset comprises first accelerometer data and first gyroscope data, wherein the second motion sensor system comprises a second accelerometer and a second gyroscope, and wherein the second motion dataset comprises second accelerometer data and second gyroscope data, and wherein generating the analysis comprises generating the analysis based upon the first and the second accelerometer data, the first and the second gyroscope data, and the first and the second multi-channel EEG bioelectrical signal datasets.
The invention relates to a system for analyzing human motion and brain activity using multiple sensor systems. The technology addresses the challenge of accurately correlating physical movement with neural activity, which is critical for applications in medical diagnostics, rehabilitation, and human-machine interfaces. The system includes at least two motion sensor systems, each comprising an accelerometer and a gyroscope, to capture motion data. Each motion sensor system generates a motion dataset containing accelerometer and gyroscope readings. Additionally, the system collects multi-channel EEG bioelectrical signal datasets from the same subject. The analysis process integrates data from both motion sensor systems and the EEG signals to generate insights. By combining accelerometer and gyroscope data from multiple sources with EEG data, the system provides a comprehensive assessment of the relationship between physical movement and brain activity. This approach enhances the accuracy of motion and neural activity analysis, enabling more precise monitoring and diagnostics in clinical and research settings.
12. The methd of claim 1 , wherein coupling the first and the second anonymized EEG bioelectrical signal datasets with the action tag comprises coupling the first and the second anonymized EEG bioelectrical signal datasets with the action tag based on the first and the second motion datasets.
This invention relates to a method for analyzing and correlating anonymized electroencephalography (EEG) bioelectrical signal datasets with specific actions. The method addresses the challenge of accurately associating brain activity data with corresponding physical or cognitive actions, particularly in scenarios where the data is anonymized to protect privacy. The method involves collecting first and second anonymized EEG bioelectrical signal datasets, which represent brain activity measurements from one or more subjects. These datasets are then coupled with an action tag, which identifies a specific action performed by the subject during the EEG recording. The coupling process is based on first and second motion datasets, which provide motion data corresponding to the actions. The motion datasets help establish a temporal or contextual link between the EEG signals and the actions, ensuring accurate association even when the EEG data is anonymized. The method may also include preprocessing the EEG and motion datasets to remove noise or artifacts, as well as synchronizing the datasets to align the brain activity with the motion data. The action tag may be derived from the motion datasets or other sources, such as user input or sensor data. The resulting coupled datasets enable further analysis, such as identifying patterns in brain activity associated with specific actions or improving machine learning models for action recognition. The method ensures privacy by maintaining the anonymity of the EEG data while still enabling meaningful correlations with actions.
13. A method for providing and aggregating bioelectrical signal data, using a EEG biosignal neuroheadset, the method comprising: defining a first action and a second action; at a first EEG sensor system of the EEG biosignal neuroheadset, automatically collecting a first bioelectrical signal dataset from a first user as the first user performs the first action and automatically collecting a second bioelectrical signal dataset from the first user as the first user performs the second action; at a motion sensor system of the EEG biosignal neuroheadset, collecting a first motion dataset from the first user as the first user performs the first action and collecting a second motion dataset from the first user as the first user performs the second action; generating a first anonymized bioelectrical signal dataset from the first bioelectrical signal dataset and a second anonymized bioelectrical signal dataset from the second bioelectrical signal dataset; coupling the first anonymized bioelectrical signal dataset with a first action tag characterizing the first action and the second anonymized bioelectrical signal dataset with a second action tag characterizing the second action; generating an analysis based upon the first and the second motion datasets first and the second anonymized bioelectrical signal datasets.
This invention relates to a system for collecting, anonymizing, and analyzing bioelectrical signal data from an EEG biosignal neuroheadset to correlate brain activity with specific actions. The method involves defining two distinct actions, such as physical or cognitive tasks, and using an EEG sensor system to automatically record bioelectrical signal datasets from a user performing each action. Simultaneously, a motion sensor system captures motion data corresponding to the same actions. The collected bioelectrical signal datasets are anonymized to protect user privacy, then tagged with labels describing the associated actions. The anonymized datasets and motion data are then analyzed to identify patterns or correlations between brain activity and the performed actions. This approach enables the aggregation of large-scale, anonymized neurodata for research, medical, or commercial applications, such as brain-computer interfaces, cognitive monitoring, or personalized health insights. The system ensures data privacy while enabling the study of brain activity in response to different actions.
14. The method of claim 13 , wherein defining the first action and the second action comprise providing a first stimulus configured to prompt the first action by the user and providing a second stimulus configured to prompt the second action by the user.
This invention relates to user interaction systems that prompt specific actions from users through stimuli. The technology addresses the challenge of ensuring users perform intended actions in a controlled and measurable way, such as in testing, training, or interactive applications. The method involves defining two distinct user actions by providing stimuli that prompt each action. The first stimulus is designed to elicit a first action, while the second stimulus is designed to elicit a second action. These stimuli can be visual, auditory, tactile, or other sensory inputs tailored to guide user behavior. The system may track and analyze the user's responses to these stimuli to assess performance, compliance, or other metrics. This approach is useful in applications like user testing, behavioral research, or adaptive interfaces where precise action prompts are needed. The stimuli can be dynamically adjusted based on user feedback or predefined criteria to optimize engagement or accuracy. The method ensures that user actions are clearly prompted and distinguishable, reducing ambiguity in interaction outcomes.
15. The method of claim 13 , wherein automatically collecting a first bioelectrical signal dataset from a first user as the first user performs the first action comprises automatically detecting when the first user initiates performance of the first action, automatically detecting when the first user terminates performance of the action, and collecting the first bioelectrical signal dataset between initiation and termination of the first action.
This invention relates to bioelectrical signal monitoring systems, specifically for capturing and analyzing bioelectrical signals during specific user actions. The problem addressed is the need for precise, automated collection of bioelectrical data during defined actions to improve accuracy and usability in applications such as medical diagnostics, fitness tracking, or rehabilitation. The method involves automatically detecting when a user begins and ends a specific action, then recording bioelectrical signals exclusively during that action. This ensures data is collected only when relevant, reducing noise and irrelevant signal interference. The system first identifies the initiation of the action, such as a movement or physiological event, then continuously monitors bioelectrical signals until the action is completed. The collected dataset is time-stamped and segmented to correspond precisely with the action's duration. This approach enhances data quality by eliminating extraneous signals captured before or after the action. It is particularly useful in scenarios where bioelectrical signals must be correlated with specific activities, such as muscle activation during exercise or neural responses to stimuli. The automated detection of action boundaries ensures consistency and reduces manual intervention, making the system more reliable for real-world applications.
16. The method of claim 13 , further comprising collecting a baseline bioelectrical signal dataset, and normalizing at least one of the first bioelectrical signal dataset and the second bioelectrical signal dataset using the baseline bioelectrical signal dataset.
This invention relates to bioelectrical signal processing, specifically for improving the accuracy of bioelectrical signal analysis by normalizing signal datasets against a baseline. The problem addressed is the variability in bioelectrical signals due to individual differences, environmental factors, or measurement conditions, which can lead to inaccurate or inconsistent analysis. The solution involves collecting a baseline bioelectrical signal dataset from a subject under controlled conditions. This baseline dataset serves as a reference for normalizing subsequent bioelectrical signal datasets. The normalization process adjusts the amplitude, frequency, or other characteristics of the collected bioelectrical signals to account for variations from the baseline, ensuring more consistent and reliable signal interpretation. The method may be applied to signals obtained from various bioelectrical sources, such as electrocardiograms (ECGs), electromyograms (EMGs), or other physiological measurements. By normalizing the signals against the baseline, the technique enhances the comparability of data across different sessions, subjects, or conditions, improving diagnostic accuracy and reducing false positives or negatives in medical or research applications. The approach is particularly useful in scenarios where signal variability could obscure meaningful physiological changes or trends.
17. The method of claim 13 , further comprising adjusting an environment of the user based upon the analysis.
This invention relates to user environment adaptation systems, specifically methods for analyzing user behavior and modifying the surrounding environment to enhance user experience or productivity. The core problem addressed is the lack of dynamic adaptation in environments to accommodate individual user needs, preferences, or physiological states in real-time. The method involves monitoring user behavior through sensors or input devices to collect data on activities, physiological responses, or environmental interactions. This data is analyzed to determine patterns, preferences, or potential discomfort indicators. Based on the analysis, the system adjusts environmental parameters such as lighting, temperature, audio levels, or device settings to optimize the user's experience. For example, if the analysis detects user fatigue, the system may dim lights, reduce ambient noise, or adjust screen brightness. Similarly, if the user is engaged in focused work, the system may minimize distractions by muting notifications or adjusting room temperature for comfort. The system may also integrate with external devices or smart home systems to extend adjustments beyond immediate surroundings, such as controlling smart blinds, adjusting HVAC settings, or modifying device configurations. The adjustments are made autonomously or with user consent, ensuring personalized and context-aware environmental control. This approach aims to improve user comfort, productivity, and well-being by dynamically adapting the environment to the user's current state or needs.
18. The method of claim 13 , wherein the first action is substantially identical to the second action.
19. The method of claim 13 , wherein generating an analysis comprises generating a comparison between the first bioelectrical signal dataset and the second bioelectrical signal dataset based upon signal analysis and data mining.
This invention relates to analyzing bioelectrical signals, such as those recorded from the heart or brain, to detect and compare physiological conditions. The method involves collecting a first bioelectrical signal dataset from a subject under a first condition and a second bioelectrical signal dataset from the same subject under a second condition. The analysis compares these datasets using signal analysis techniques, such as filtering, frequency analysis, or waveform morphology assessment, and data mining methods, such as pattern recognition or statistical modeling. The comparison identifies differences or similarities between the signals, which may indicate changes in physiological state, disease progression, or treatment efficacy. The method may be applied in medical diagnostics, monitoring, or research to assess how bioelectrical activity varies under different conditions, such as rest versus exercise or before and after a medical intervention. The analysis can be automated or semi-automated, providing quantitative insights into bioelectrical signal dynamics.
20. A system for providing and aggregating bioelectrical signal data comprising: a first EEG biosignal neuroheadset for collecting a first bioelectrical signal dataset and a first motion dataset from the first user as the user performs an action, wherein the first EEG biosignal neuroheadset comprises: a first EEG sensor system for collecting the first bioelectrical signal dataset; and a first motion sensor system for collecting the first motion dataset; a second EEG biosignal neuroheadset for collecting a second bioelectrical signal dataset and a second motion dataset from the second user as the user performs the action, wherein the second EEG biosignal neuroheadset comprises: a second EEG sensor system for collecting the second bioelectrical signal dataset; and a second motion sensor system for collecting the second motion dataset a remote server comprising: a receiver for receiving the first and the second bioelectrical signal datasets, an anonymizer for transforming the first bioelectrical signal dataset and the second bioelectrical signal dataset into a first and a second anonymized bioelectrical signal dataset, a coupler for coupling the first anonymized bioelectrical signal dataset with an action tag characterizing the action, and to couple the second anonymized bioelectrical signal dataset with the action tag, and an analyzer to for generating an analysis based upon the first and the second motion datasets and the first and the second anonymized bioelectrical signal dataset.
The system is designed for collecting, anonymizing, and analyzing bioelectrical signal data from multiple users performing the same action. The technology addresses the challenge of aggregating and interpreting brain activity and motion data while preserving user privacy. Each user wears an EEG biosignal neuroheadset equipped with EEG sensors to capture brain activity and motion sensors to track physical movement during the action. The system includes a remote server that receives bioelectrical signal datasets from multiple users. The server anonymizes the data to protect user identity before coupling it with an action tag that describes the performed action. The server then analyzes the anonymized bioelectrical signals alongside motion data to generate insights. This approach enables the study of brain activity patterns and physical movements across users without compromising individual privacy. The system supports applications in neuroscience research, cognitive performance analysis, and personalized health monitoring.
21. The system of claim 20 , wherein at least one of the first biosignal detector and the second biosignal detector comprises a sensor system configured to automatically sense an indicator of the action.
The invention relates to a biosignal detection system designed to monitor and analyze physiological signals from a user. The system addresses the challenge of accurately detecting and interpreting biosignals, such as those related to user actions, to improve health monitoring, user interaction, or other applications requiring real-time physiological data. The system includes at least two biosignal detectors, each configured to capture distinct or complementary biosignals from the user. These detectors may be positioned at different locations on the user's body or within an environment to ensure comprehensive signal acquisition. At least one of the detectors incorporates a sensor system that automatically senses an indicator of a specific action performed by the user. This sensor system may include one or more sensors capable of detecting movement, muscle activity, or other physiological changes associated with the action. The system processes the detected signals to identify patterns or changes that correspond to the action, enabling real-time or delayed analysis. The detectors may use various sensing technologies, such as electromyography (EMG), electroencephalography (EEG), or motion sensors, depending on the type of biosignals being monitored. The system may also include processing components to filter, amplify, or analyze the signals, ensuring accurate detection and interpretation. By integrating multiple detectors and advanced sensing capabilities, the system provides a robust solution for monitoring user actions through biosignal analysis.
22. The system of claim 21 , wherein the sensor system comprises at least one of a GPS sensor, an accelerometer, and an optical sensor.
23. The system of claim 20 , wherein the first biosignal detector and the second biosignal detector comprise personal electroencephalogram signal collection devices.
The system relates to biosignal monitoring, specifically for detecting and analyzing electroencephalogram (EEG) signals from multiple individuals. The problem addressed is the need for accurate, non-invasive collection of brain activity data from multiple subjects simultaneously, which is critical for applications in neurology, cognitive research, and brain-computer interfaces. The system includes at least two biosignal detectors, each configured as a personal EEG signal collection device. These detectors are designed to capture electrical activity generated by the brain, providing real-time or stored data for analysis. The detectors may be wearable or portable, allowing for flexible deployment in clinical, research, or consumer settings. The system may also include processing components to filter, amplify, and interpret the EEG signals, ensuring high-fidelity data collection. By using multiple detectors, the system enables comparative studies or collaborative monitoring of brain activity across different individuals. The design emphasizes ease of use, scalability, and compatibility with existing EEG analysis tools. This approach improves the reliability and versatility of EEG-based diagnostics and research.
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January 16, 2018
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